ORIGINAL ARTICLE

ARMin: a robot for patient-cooperative arm therapy

Tobias Nef Æ Matjaz Mihelj Æ Robert Riener

Received: 22 December 2006 / Accepted: 1 July 2007

� International Federation for Medical and Biological Engineering 2007

Abstract Task-oriented, repetitive and intensive arm

training can enhance arm rehabilitation in patients with

paralyzed upper extremities due to lesions of the central

nervous system. There is evidence that the training duration

is a key factor for the therapy progress. Robot-supported

therapy can improve the rehabilitation allowing more

intensive training. This paper presents the kinematics, the

control and the therapy modes of the arm therapy robot

ARMin. It is a haptic display with semi-exoskeleton

kinematics with four active and two passive degrees of

freedom. Equipped with position, force and torque sensors

the device can deliver patient-cooperative arm therapy

taking into account the activity of the patient and sup-

porting him/her only as much as needed. The haptic display

is combined with an audiovisual display that is used to

present the movement and the movement task to the

patient. It is assumed that the patient-cooperative therapy

approach combined with a multimodal display can increase

the patient’s motivation and activity and, therefore, the

therapeutic progress.

Keywords Robotics � Haptic device � Exoskeleton �Rehabilitation � Arm therapy

1 Introduction

1.1 Clinical background and rationale for arm therapy

Patients with paralysed upper extremities due to lesions of

the central nervous system, e.g. after stroke, traumatic

brain or spinal cord injury, often receive arm therapy. The

goal of this therapy is to recover motor function, improve

movement co-ordination, learn new motion strategies, so

called trick movements, and to prevent secondary com-

plications, such as muscle atrophy, osteoporosis, joint

degeneration and spasticity.

Several studies prove that arm therapy has positive

effects on the rehabilitation progress of stroke patients

(see [25] for review). Several groups observed that longer

daily training sessions and longer overall training periods

have a positive effect on the motor function of the arm

[18, 19, 33]. In a meta-analysis comprising nine controlled

studies with 1,051 stroke patients, Kwakkel et al. [17]

showed that increased training intensity yields moderate

positive effects on neuromuscular function and activities

of daily living (ADL). The study did not distinguish

between upper and lower extremities. The conclusion that

the rehabilitation progress depends on training intensity

and training duration supports the application of robot-

aided arm therapy.

There is further evidence that machine-delivered thera-

pies can enhance the treatment [1, 5, 20, 21, 32]. Consi-

dering upper limb therapies, a robot can provide assistance

with varying degrees of compensatory movements for the

affected limb. There is evidence that the recovery is more

effective when the patient actively participates in the

training [13]. It is hypothesized that for a successful

rehabilitation, it is crucial to motivate the patient to

actively participate during the training exercises.

T. Nef (&) � M. Mihelj � R. Riener

Sensory-Motor Systems (SMS) Laboratory,

ETH Zurich, TAN E, Tannenstrasse 1,

8092 Zurich, Switzerland

e-mail: nef@mavt.ethz.ch

URL: www.armin.ethz.ch

T. Nef � M. Mihelj � R. Riener

Spinal Cord Injury Center, University Hospital Balgrist,

University Zurich, Zurich, Switzerland

123

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DOI 10.1007/s11517-007-0226-6

Medical and Biological Engineering and Computing, Vol. 45, 2007, p.p. 887-900

New actor, sensor, and control strategies can make

robots ‘‘intelligent’’ providing measurement data to assess

the rehabilitation progress, and to gain insight into the

underlying pathology and allowing the patient to actively

participate in the training.

1.2 Rationale for robot-aided arm therapy

One-to-one manually assisted training has several limita-

tions. The training is labour-intensive, time consuming,

and, therefore, expensive. The disadvantageous conse-

quence is that the training sessions are often shorter than

required for an optimal therapeutic outcome. Finally,

manually assisted movement training lacks repeatability

and objective measures of patient performance and

progress.

In contrast, with automated, i.e. robot-assisted, arm

therapy, the duration and number of training sessions can

be increased, while reducing the number of therapists

required per patient. A long-term automated therapy

appears to be the only way to make intensive arm training

affordable for clinical use. As the actual version of the

ARMin-robot is still in the prototype phase, safe operation

requires that one therapist supervises the training (c.f.

2.10). In the future, it will be possible that the patient can

be treated by the device with less supervision. Therefore,

the therapist will be able to manage several robotic devices

or he will be able to do other work besides. Thus, personnel

cost can be reduced. Furthermore, the robot provides

quantitative measures, thus, supporting the observation and

evaluation of the rehabilitation progress.

Several groups have proposed robots to assist physio-

therapy and rehabilitation of the upper limbs (see [26, 27]

for review). The devices provide a varying degree of

assistance to the patient’s movements, ranging from no

assistance if the patient has sufficient voluntary control, to

full assistance, where the patient can behave passively.

New control strategies have been introduced that allow the

machine to comply with forces exerted by the patient

enabling new possibilities for rehabilitation while guaran-

teeing safety for the patient [4, 27, 28].

1.3 Requirements for a rehabilitation robot

In the design and application of rehabilitation robots,

medical aspects must be taken into account to ensure a

successful training. It is crucial that the robot is adaptable

to the human limb in terms of segment lengths, range of

motion and the number of degrees-of-freedom (DOF). A

high number of DOF allows a wide variety of movements,

with many anatomical joint axes involved. However, this

can make the device complex, inconvenient and expensive.

It remains an open issue to assess how many DOF are

optimal for upper limb rehabilitation. The question is

whether therapeutic outcome can be maximized, if the

robot acts on the entire extremity rather than on single

joints only. To answer this question would require a clin-

ical study of an enormous sample size performed with

various devices. However, there is evidence that a therapy

focussing on activities of daily living (ADL) not only

increases the patient’s motivation but also yields an

improved therapeutic outcome, compared to therapies

focussing on single joint movements [2, 22, 25, 34]. To

allow ADL training, a robot must be able to move the

patient’s arm in all relevant degrees of freedom and to

position the human hand at any given point in space. This

can be achieved by an end-effector-based robot or by an

exoskeleton-type device.

End-effector-based robots are connected with the

patient’s hand or forearm at one point. From a mechanical

point of view, these robots are easier to realize and thus,

many research groups work with end-effector-based devi-

ces [7, 9, 16]. In contrast, the structure of exoskeleton

robots resembles the human arm anatomy [29]. Conse-

quently, the arm is attached to the exoskeleton at several

points. Adaptability to different body sizes is easier in an

end-effector-based system, i.e. where the robot moves the

arm by inducing forces only on the patient’s hand. In

contrast, exoskeletal systems are more difficult to adjust,

because each robot link must be adjusted to the corre-

sponding patient arm segment. However, the advantage of

an exoskeleton system as compared to the end-effector-

based approach is that the arm posture is statically fully

determined. Torques applied to each joint can be controlled

separately and hyperextensions can be avoided by

mechanical stops. The possibility to control torques in each

joint separately is essential, e.g. when the subject’s elbow

flexors are spastic. This involuntary muscle activation

results in an increased resistance against movements. To

overcome the resistance, elbow torque up to 20 Nm is

necessary (Table 1). This must not induce any reaction

torques or forces in the shoulder joint, which can be

guaranteed by an exoskeleton robot but not by an end-

effector-based one. This is important because the shoulder

girdle is a rather instable joint and the head of the humerus

bone is held in its position by muscles and tendons and not

by ligaments and bones. If one applies high shear forces to

the shoulder joint, humerus head dislocation can occur.

That’s the reason why therapists use both hands when

they mobilize a spastic elbow joint. With the goal to avoid

exercise forces to the shoulder, one hand holds the lower

arm while the other hand holds the upper arm—comparable

to exoskeleton robots with a cuff fixed to the lower arm and

a cuff fixed to the upper arm.

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1.4 Patient cooperative arm therapy and specific

research aims

Since the therapy progress depends on training intensity

and training duration, the motivation of the patient turns

out to be a key factor for an efficient rehabilitation [14].

The patient needs to get motivated to contribute actively to

the movement, which may enhance recovery. In addition,

to make longer training duration and more training sessions

possible, it is crucial that the patient enjoys the therapy,

stays concentrated, and does not get bored.

The so called ‘‘cooperative arm therapy’’ discussed in

this paper takes into account the following key aspects that

motivate the patient: (1) the device stimulates the three

most important sensory modalities of the patient, i.e. the

haptic, visual and auditory senses and (2) the robot and

the patient cooperate and interact, i.e. the robot assists the

patient just as much as needed to perform a particular

movement task.

While many clinical studies (see [26] for review) have

been conducted with end-effector-based robots with limi-

ted possibilities to control position and orientation of the

human arm in the three-dimensional space, not much

clinical evidence has been reported from work with actu-

ated arm-exoskeleton robots. The key aspects of this pro-

ject are that the ARMin device allows precise joint

actuation and 3D movement of the arm, that the device

allows to work in ‘‘patient cooperative’’ control modes and

that the device includes a comprehensive audiovisual user

interface.

2 Methods

2.1 Specifications

Training of ADL includes tasks like eating, drinking,

combing hair, etc. For most of these ADL tasks, the hand

has to reach a point in space, grasp an object, and then

control position and orientation of the object until the task

is completed. Therefore, the robot must be able to support

movements of the shoulder, the elbow, and the wrist.

Approximating the shoulder by a three-DOF ball-and-

socket joint, and allowing elbow flexion/extension, pro/

supination of the lower arm and wrist flexion/extension,

results in a device with at least six active DOF. To simplify

the task, our first prototype was built only with four active

DOF supporting the movements of the shoulder joint and

elbow flexion/extension.

The range of motion (ROM) must match as close as

possible the ROM of the human arm [35]. In order to obtain

a satisfactory control performance of patient-cooperative

control strategies, which are based on impedance and

admittance architectures, the robot must have low inertia,

low friction and negligible backlash. Furthermore, the

motor/gear unit needs to be back drivable. Back-drive-

ability is required for good performance of the impedance

control [10–12] and it is advantageous for the safety of

exoskeleton robots (cf. 2.10).

The required velocities and accelerations have been

determined by measuring the movements of a healthy

subject during two ADL tasks (eating soup and manipu-

lating a coffee cup). These values served as inputs for a

simple dynamic model applied to estimate the required

joint torques. In order to ensure that the robot will be strong

enough to overcome resistance from the human against

movements due to spasms and other complications that are

difficult to model, rather high values have been selected

(Table 1). The required end-point payload is 1 kg and end-

point position repeatability is 10 mm. These values allow

manipulation of objects like a coffee cup.

Furthermore, it is required that the robot is easy to

handle and that safety is always guaranteed for both patient

and therapist.

2.2 Kinematics

A semi-exoskeleton solution has been selected for the

mechanical structure of the robot called ARMin (Fig. 1).

The robot is fixed via an aluminium frame at the wall with

the patient sitting in a wheelchair, placed beneath. The

patient’s torso is fixed to the wheelchair with straps and

bands (Fig. 8). ARMin comprises four active and two

passive DOF in order to enable elbow flexion/extension

and spatial shoulder movements [24]. The distal part is

characterized by an exoskeleton structure, with the

patient’s lower and upper arm placed inside orthotic shells.

Table 1 Requirements for the range of motion (ROM), velocity and the maximal torques

Axis ROM Velocity (�/s) Acceleration (�/s2) Torque (Nm)

Axis 1 (Vertical shoulder rotation) 100� to �135� 71 103 20

Axis 2 (Horizontal shoulder rotation) 135� to �45� 60 129 20

Axis 3 (Internal/external shoulder rotation) �50� to 95� 150 245 10

Axis 4 (Elbow flexion/extension) 0� to 135� 91 116 20

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The exoskeletal part drives the internal/external rotation of

the upper arm and the elbow joint, whereas horizontal and

vertical shoulder rotation is actuated by an end-effector-

based part connecting the upper arm with the wall-mounted

axes 1 and 2.

The robot becomes statically determined only in com-

bination with the human arm. To prevent the robot from

falling down and to avoid unfavourable shear forces and

torques onto the human shoulder, the weight of the robot is

compensated by a passive counterweight. This is achieved

by a counterweight of 2.5 kg that is connected to the robot

via a cable guided by two pulleys.

This end-effector-type kinematics for the shoulder joint

has been selected as exoskeleton mechanics would make it

difficult to get the robot axes in alignment with the ana-

tomical axes of the human, and misalignments would

mechanically overstress and potentially harm the shoulder.

2.3 Mechanics and actuation

The vertically oriented linear motion module (axis 1)

drives vertical shoulder rotation (flexion/extension and

abduction/adduction) movements. Horizontal shoulder

rotation (horizontal flexion/extension and horizontal

abduction/adduction) is realized by a backlash-free and

back-drivable harmonic drive module attached to the slide

of the linear motion module (Table 2).

The interconnection module connects the horizontal

shoulder rotation drive with the upper arm rotary module

via two hinge bearings. Elbow flexion/extension is realized

by a harmonic drive rotary module.

Internal/external shoulder rotation is achieved by a

special custom-made upper arm rotary module that is

connected to the upper arm via an orthotic shell. For easy

access to the patient’s arm, the module is made out of two

half-cylinders. An inner half-cylinder (Fig. 2) is guided by

32 ball bearings fixed to the exterior wall. It is actuated by

three steel cables fixed to the two ends of the cylinder and

rolled around the extension of the motor shaft. This guid-

ance allows transfer of static loads in several DOF while

remaining backlash-free and enabling low friction circular

motion. Custom-made cuffs (Fig. 3) ensure a comfortable

fixation of the patient to the arm rotary module.

2.4 Adaptation to different body sizes

To apply the robot to patients of different sizes, the lengths

of exoskeleton segments need to be adjustable. Table 3 and

Fig. 4 show the ranges of all possible adaptations.

In order to fix the shoulder position of the patient under

the fulcrum at axis 2 (horizontal shoulder rotation), an

individually adjustable belt system was constructed

(Fig. 9). An optional hand support (Fig. 4) can be added to

allow comfortable hand posture.

2.5 Sensors and control hardware

The four exoskeleton actuators are equipped with optical

incremental sensors and redundant potentiometer-based

sensors for position measurements. A six-DOF force-tor-

que sensor beneath the horizontal arm rotation module

measures forces and torques of the shoulder joint (axis 1–

3). The elbow torque is measured by a separate torque

sensor (Fig. 1).

The controller runs on a Matlab/Simulink XPC target

(The Mathworks, Inc., USA) computer with 1 ms loop

time. Four analogue channels provide inputs for the current

amplifiers (Maxon 4-Q-DC Servoamplifier ADS 50/5;

Maxon Motor AG, Switzerland). The four-encoder signals,

the analogue signals from the redundant potentiometer-

Fig. 1 Mechanical structure of ARMin [24]

Table 2 Actuation of the four

axesAxis Gear Motor type

Axis 1: Vertical shoulder rotation Ball screw, 10 mm/rot Maxon RE40, brushed DC

Axis 2: Horizontal shoulder rotation Harmonic drive 1:100 Maxon RE35, brushed DC

Axis 3: Internal/external shoulder rotation Cable drive 1:24.5 Maxon RE40, brushed DC

Axis 4: Elbow flexion/extension Harmonic drive 1:100 Maxon RE35, brushed DC

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based position sensors are interfaced to the Sensoray 626 I/

O (Sensoray Company Inc., USA) interface card.

The graphical user interface runs on a computer with

Windows operating system (Microsoft Corporation, USA)

and is connected with the real time target by a local area

network using TCP/IP protocol.

Two graphical displays are used: (1) the therapist robot

interface (TRI)—a standard LCD monitor display, and (2)

the patient robot interface (PRI)—a double-projector sys-

tem with polarized images to generate graphical 3D

scenarios for the patient. The TRI contains a dialog-based

graphical user interface, which allows the therapist to enter

the patient’s data, choose the therapy mode, display the

actual patient performance and save the therapy log file.

These data are not shown to the patient, who, instead, looks

onto the screen of the PRI, where the tasks are displayed by

animated 3D scenarios (Fig. 6).

2.6 Dynamic model and basic control issues

A dynamic model of the robot and the human arm has been

developed to simulate and evaluate different control

schemes. An inverse dynamic and a direct dynamic model

have been derived using Lagrange methodology. The direct

dynamic model is given by

€q ¼ M�1ðqÞðs� Cðq; _qÞ _q� GðqÞÞ ð1Þ

where M is the inertia matrix, Cðq; _qÞ _q is the vector of

Coriolis and centrifugal torques, GðqÞ is the vector of

gravity torques and q is the vector of joint positions. The

elements of the robot have been modelled in 3D and the

matrices of inertia and the centres of gravity have been

calculated using the CAD numerical finite element calcu-

lation (AutoCAD, Autodesk, USA). The upper arm and the

lower segments of the human arm have been modelled as

conical frustums with homogeneous mass, equal to that of

water [8].

The inverse dynamic model can be expressed by

s ¼ MðqÞ€qþ Cðq; _qÞ _qþ GðqÞ ð2Þ

where s is the vector of joint torques. In general, the

following torque contributions appear [31]

Fig. 2 Upper arm rotary module for internal/external shoulder

rotation (opened to allow view inside)

Fig. 3 Cuff that connects the rotary module with the human upper

arm

Table 3 Ranges of the adaptations to different body sizes

Label Description Range (cm)

a Shoulder height of the sitting patient 90–110

b Upper arm length 27–42

c/d Lower arm length 20–32

e Wrist circumference 16–24

F Upper arm circumference 20–40

G Optional hand support –

Fig. 4 Six possibilities to adapt the robot to different body sizes

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si ¼ smi � sfi � shi ði ¼ 1; . . .; 4Þ ð3Þ

where si is the driving torque of joint i, smi is the torque of

joint i delivered by the motor, sfi is the torque of joint i due

to joint friction and shi is the torque of joint i caused by the

human. The velocity dependent part of friction has been

identified and compensated.

A PD controller with a computed torque feed forward

portion has been implemented for position control and

evaluated by simulation (Fig. 5). Three different strategies

have been compared regarding their control error and the

computational effort with regard to the number of opera-

tions needed per time step. Sample time was fixed to 1 ms.

In the first strategy, the PD values where selected by

simulation and remained constant while the feed forward

part was calculated by the inverse dynamic approach (Eq.

2) (computed torque control). In the second strategy, the

feed forward part was simplified to the gravity part leading

to

sff ¼ GðqÞ ð4Þ

And in the third strategy the feed forward part has been

set equal to sff ¼ 0, which reduces the strategy to a simple

PD controller. Sinusoidal trajectories have been used for

these experiments, while in the real application the tra-

jectory generation described in Chap. 2.7 has been used.

The model served also to investigate stability of the robot

interacting with the human. For this purpose, a human that

produces perturbations of different frequencies and

amplitudes has been simulated.

2.7 Trajectory generation for the mobilisation therapy

Mobilisation therapy is based on repetitive movements of

the human arm on a patient-specific trajectory while the

patient remains passive. This can prevent joint degenera-

tion, preserve the patient’s mobility and joint flexibility,

and reduce spasticity. The mobilisation therapy is executed

in two steps. First, the therapist moves the patient’s arm

together with the robot on a desired trajectory. The exact

shape of the movement (ranges, speed) can be solely

defined by the therapist taking into consideration the

individual impairment of the patient. During the recording

phase, the robot’s gravity and friction are compensated so

that the therapist only feels the forces and torques neces-

sary for moving the human arm. Once the trajectory is

recorded, the relevant points of the trajectory are deter-

mined in order to enable calculation of a smooth and

‘‘human-like’’ movement path. This is required as the tra-

jectory performed by the therapist is often shaky. Simple

low-pass filtering is not appropriate, as it would cut the

movement at the extremes. In the second step, the robot

repeats the trajectory with an adjustable velocity.

Jung-Hoon et al. [15] developed a simple method to

extract a small number of way-points for the trajectory of a

mobile robot. This method has been adapted in the way that

turning points of the recorded position data are detected

and used as via points. Once these points are detected, a

smooth trajectory connecting them is generated by a mini-

mum jerk approach [6]. This approach is quite common to

describe the kinematics of smooth human arm reaching

movements. The resulting trajectory serves as input for the

position controller (c.f. 2.6).

2.8 Audiovisual display

An appropriate audiovisual display is imperative for the

game supported therapy (cf. 2.9) where movement tasks

need to be displayed to the patient. The mobilisation

therapy (cf. 2.6) could also be performed without any

audiovisual feedback. However, showing a virtual arm can

help the patient to realize his or her arm posture.

As the robot is able to perform ADL-related movements

in space, a stereographic display is used to present virtual

objects in three dimensions. The screen is rather large

(2 m · 2.7 m) so that even patients with mild visual

impairments can recognize the scenarios and tasks, and

Fig. 5 Computed torque

position control loop for the

mobilisation therapy

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generate a feeling of presence. Additional sound can help

to increase the feeling of presence during the game sup-

ported therapy.

Two projectors with polarizing filters are used to project

stereographic images via back projection onto the screen.

Patients wear lightweight passive polarizing glasses to

see 3D images. The Open Inventor clone Coin 3D

(http://www.coin3D.org) has been used as a scene graph. In

combination with the quad buffer of the NVIDIA Quadro

Fx 4000 graphic card (NVIDIA Corporation, USA) ste-

reoscopic images are generated without any additional

programming effort.

The virtual environment (VE) consists of a graphical, an

acoustic, and a physical model of the scene. The physical

model is required to determine force feedback signals in

order to allow the patient to physically interact with the

VE. It is implemented on the real time computer (XPC

Target) and updated with a sampling rate of 1 kHz. The

graphical model is implemented with Coin 3D and runs on

a Windows computer with a sampling rate of approxi-

mately 100 Hz.

Several different scenarios have been implemented.

During the mobilisation therapy, an avatar figure showing

the actual posture of the patient’s arm is presented (Fig. 6

upper left). A ball game scenario (Fig. 6 upper right)

includes a ball rolling down an inclined table and a hand

connected to the handle. The ball is reflected by the walls

and the patient’s task is to catch the ball with the handle.

The colour of the handle changes depending on the per-

formance of the patient (Fig. 11). The sounds of the rolling

ball and the collisions with the wall and the handle are

displayed to increase the level of realism. A labyrinth game

scenario has been implemented (Fig. 6, lower left) with a

red ball indicating the cursor that moves according to the

patient’s spatial hand movement. Force feedback is pro-

vided whenever the cursor touches the wall of the laby-

rinth. To motivate the patient, objects can be collected on

the way from the bottom to the top of the labyrinth.

In the ADL training mode, the patient is asked to grasp

an object by approaching it with the virtual hand and to put

it onto the table (Fig. 6, lower right). The control strategy

for these kinds of tasks is based on the minimal interven-

tion principle, which allows an efficient exploitation of task

space redundancies and results in user-driven movement

trajectories [23]. The basic idea of this approach is that

deviations from the trajectory are corrected only when they

interfere with the task performance. In other words: if the

patient is doing ‘‘better’’ than the robot, then the robot lets

the patient do so.

2.9 Control scheme for the ball game

A simple ball game has been implemented to demonstrate

and validate the concept of game-supported therapy. The

task is to catch a virtual ball rolling down an inclined

virtual table (Fig. 6, upper right). The ball movement in y

direction (vertical direction on the screen) is governed by

Newton’s law:

vyball¼ v0 � g sinðaÞ ð5Þ

where v0 is the initial velocity of the ball, g is the gravity

acceleration and a is the inclination of the virtual table. The

initial velocity v0 and inclination a can be adjusted for each

Fig. 6 Scenarios for the arm

therapy. Upper left Movement

therapy. Upper right Game

supported therapy (ball game).

Lower left Game supported

therapy (labyrinth game). Lowerright ADL training scenario

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patient in order to increase or decrease the difficulty of the

game.

The control strategy (Fig. 7) is based on an impedance

controller, where the assisting force Fxref is determined,

based on the relative distance between the ball and the

human hand position. The force in horizontal direction

Fxref, that the robot exerts onto the patient’s arm to push

him towards the ball position is:

Fxref ¼0

K xball� xhandð Þ � ð1� yballÞ�Bdxhand

dt

�if yball [0:5if yball�0:5

ð6Þ

K is a constant value that can be adjusted by the therapist

and B is a damping factor. Typical values are K = 10 N/m

and B = 0.01 Ns/m.

During the first part of the rolling-down sequence

(yball > 0.5), the force Fxref is always zero. This kind of

delay gives the patient time to try to bring his hand to

the ball position without robotic support. In the second

part of the sequence (yball � 0.5), the force Fxref is

proportional to the distance between the hand and the

ball (xball–xhand) and to the factor (1–yball), and therefore

zero if the patient was able to bring his hand to the ball

position during the first sequence. If not, then the sup-

porting force rises when the ball approaches the zero

level (yball ? 0).

2.10 Passive and active safety

Safety was a main issue during the design of the robot.

Passive safety features (no sharp edges, mechanical end

stops to guarantee that no joint can exceed the anatomical

range of motion, etc.) are combined with active safety

features. Four redundant absolute position-sensing poten-

tiometers—one for each joint—allow detecting malfunc-

tion of a position sensor. Several surveillance routines are

implemented in the software. These include current and

speed monitoring, a robot self-collision detection algorithm

and several watchdog systems.

Whenever an abnormal event is detected, the safety

circuit immediately cuts the power of the motor drives. As

the robot is designed with a passive weight compensation

system (pulley, rope, and counterweight, see Fig. 8) it does

not collapse after power loss. Since all drives are back

drivable, the robot can easily be moved manually by a

therapist in order to release the patient from a potentially

uncomfortable or dangerous posture.

Last but not least, the physiotherapist always observes

the training holding a deadman button in his hand.

Releasing the button interrupts the motor power and stops

the robot immediately. This can also be achieved by

pressing one of the three emergency stop buttons. It is

expected that future robots will not require a permanent

supervision and that the deadman button could be omitted.

Beside patient safety, also safety of the therapist needs

to be considered. As the robot does not know the position

of the therapist, it is important that the therapist is aware of

the danger of collisions with the robot. Nevertheless, the

Human

Robot

q

q

τ

hτ

Gravity compensation

gravτ

Friction compensation

frictτ

F/T Sensor

TJP

Bs

+

+ ++

++ +

refxF

xf−

Kbally

Physical model of the inclined

plane and of the ball

+ −

bally

ballx

handx

)0( handx

)(ballball yx

visual information

graphical display

x

y

K+

0 5.0 1

1

constant gain

TJ −)(qk

direct kinematics

−

Fig. 7 Control strategy and

signal flow of the ball game

Fig. 8 ARMin prototype that has been used for all experiments

described in this paper

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probability of a severe accident is low because of the fact

that the maximal speed of the robot is limited by the sur-

veillance circuit. Furthermore, the therapist can always

interrupt the motion by releasing the deadman button. A

detailed risk analysis shows that the risk for a patient and a

therapist using the robot is acceptable with respect to the

expected rehabilitative benefit to the patient.

3 Results

ARMin has been installed at Balgrist University Hospital

in Zurich, Switzerland (Fig. 8). After approval of the ethics

committee of Zurich, the device first has been tested with

healthy subjects. Then, a pilot study including eight

hemiplegic and three incomplete spinal cord injured sub-

jects has been carried out, followed by three clinical single-

case studies with chronic stroke patients performing

intensive robotic training of longer duration. The tests with

healthy subjects and the pilot study served to prove the

functionality of the device, without looking at possible

improvements in the motor performance of the patients.

Possible improvements have then been assessed by the

clinical studies with the three chronic stroke patients

(publication in preparation).

3.1 Control scheme and stability

The dynamic model was used to simulate the overall system

and to evaluate the performance of three different control-

lers: Computed torque, PD in combination with gravity

compensation, and PD alone. Sinusoidal reference trajec-

tories (T = 2ps) where selected as reference trajectories for

all four axes. The mean position error was calculated for

every axis and for all axes together. Three different condi-

tions where selected. First, the ideal model was used, where

ideal means that the plant model (direct dynamic model of

robot and human) was equal to the model used for feed

forward torque calculation (inverse dynamic model).

Second, the ideal model was used with sensor discretisation.

As the encoders deliver a digital signal, no noise was

introduced. And third, the direct dynamic model was fal-

sified by increasing the mass of the human arm by 30%,

simulating modelling errors. The PD-values where for all

cases the same and they have been determined by simula-

tions using the dynamic model (Axis 1: P = 10,000 N/cm,

D = 50 Ns/cm; Axis 2: P = 1,000 Nm/rad, D = 10 Nms/

rad; Axis 3: P = 1,000 Nm/rad, D = 10 Nms/rad; Axis 4:

P = 300 Nm/rad, D = 5 Nms/rad). For all simulations,

passive behaviour of the human was assumed. Table 4

summarises the results.

The third condition reflects reality best. In this condi-

tion, the computed torque controller does not show sig-

nificantly better results than the PD controller with gravity

compensation, although the computational effort was much

higher. In contrast, the pure PD controller required only a

few operations, whereas it showed large errors. As a

compromise, the PD controller with gravity compensation

was chosen and implemented in the robot controller

hardware.

In order to find out whether the controller stays stable

when the user applies rhythmic disturbances, sinusoidal

disturbances acting onto one axis of the robot have been

simulated and the position and the velocity error of the

simulated robot have been observed. The frequencies of the

Table 4 Mean position errors of different control schemas

PD +

computed

torque

PD + gravity

compensation

PD

Number of operations

per step

8000 150 20

Position error with ideal

plant model

0.00007� 0.06� 0.5�

Position error with ideal plant

model with sensor discretisation

0.007� 0.06� 0.5�

Position error with falsified plant

model with sensor discretisation

0.05� 0.08� 0.5�

05

1015

2025

30 0

5

10

15

20

25

30

0

0.1

0.2

0.3

0.4

Amplitud

e [Nm]

Frequency [Hz]

Pos

ition

err

or [

rad]

0

10

20

30 0

10

20

300

0.5

1

1.5

2

2.5

3

Amplitude [Nm]

Frequency [Hz]

Vel

ocity

err

or [

rad/

s]

Fig. 9 Left Simulated position

error with sinusoidal

disturbances produced by the

human. Right Simulated

velocity error with sinusoidal

disturbances produced by the

human. The figure shows when

the simulated robot becomes

marginally stable, which means

that the output is still bounded

but vibrations occur

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disturbances varied from 0 to 30 Hz with amplitudes

varying from 0 to 30 Nm, and the disturbances have been

injected at the would point where the user exercises forces

onto the robot. For this simulation, the saturation of the

motors has been set to 25 Nm.

A series of simulation studies leads to the result that the

position error stays small while the velocity error varies with

the frequency and amplitude of the disturbance (Fig. 9).

3.2 Functional tests with healthy subjects

Within the tests with healthy subjects, the ROM, the con-

trol and the overall functionality have been validated

(Table 5). These tests included 20 min’ mobilisation ther-

apy and 40 min’ game therapy.

3.3 Pilot study with patients

A pilot study was carried out to validate the patient comfort

and handling, i.e. whether the robot is adjustable to dif-

ferent patient sizes, whether the cuffs are comfortable for

the patients, and whether the patients are able to perform

the movement tasks. Furthermore, the patient acceptance

and motivation were interrogated.

Using the same protocol as for healthy subjects, the

robot has been tested with 11 patients for a cumulative

duration of more than 76 h. The robot could easily

accommodate subjects with body sizes between 155 and

192 cm. The fixation of a patient in the robot takes

approximately 5 min.

Figure 10 shows an example for a trajectory recorded by

a therapist and the resulting trajectory after applying the

minimum jerk approach. Note that the extreme values of

both trajectories correspond with each other.

Figure 11 shows the results of the game therapy with

two different hemiplegic patients. A ball has been dis-

played falling down on the graphical display. The patient

had to catch it by moving the virtual hand (Fig. 6, upper

right). Subject A was able to catch the ball mostly without

any robot support. The robot assisted patient B by pushing

the patient with the force Fxref towards the ball (Fig. 11), if

the patient was not able to catch the ball himself (cf. Chapt.

2.9). Note that the robot support is always delayed rela-

tively to the change in the horizontal ball position. In this

Table 5 Technical data of the device

Payload (Endpoint) 2 kg

Weight (excl. controller hardware, frame), approx. 6.5 kg

Repeatability (endpoint) ±3 mm

Stiffness (endpoint)a >714 N/m

Minimal apparent mass (endpoint) 240 g

Bandwidth for small endpoint movements (0.1 m),

according to Ellis et al. [3]

2.1 Hz

Axis data (subject body size 181cm): Range: Precision: Stiffness of the

motor/gear unit:

Max

torquesbMobilisation

torquesc

Axis 1 (Vertical shoulder rotation) 44� to �59� 0.1� 136 kN/m 57 Nm 10.15 Nm

Axis 2 (Horizontal shoulder rotation) 130� to �50� 0.1� >1,600 Nm/rad 52 Nm 9.36 Nm

Axis 3 (Internal/external shoulder rotation) �60� to 95� 0.5� 229 Nm/rad 11 Nm Not measured

Axis 4 (Elbow flexion/extension) 5� to 119� 0.1� >1600 Nm/rad 27 Nm 24.36 Nm

a Stiffness measured at the endpoint by applying 20 N while the motors are position controlled and a dummy arm was fixed in the deviceb Torques measured during maximum voluntary contractions of healthy personsc Torque values necessary for mobilisation. Maximum values that occurred during 60 mobilisation movements carried out on three stroke

patients

Fig. 10 Recorded trajectories during teach mode and the derived

minimized jerk trajectory

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way, the subject is first encouraged to voluntarily move the

virtual hand toward the target. Only when the height of the

ball decreases to a level that would not allow the subject to

catch the ball, the robot support is activated and guides the

arm to the target position.

An important goal was to assess the patient motivation

for this kind of therapy. Thus, five patients had to fill out a

questionnaire after the therapy sessions. As most of them

where not able to read and write, they where interrogated

by an independent person. Five questions where asked and

the patients could answer with a number between 1 and 6,

where 1 means ‘‘not at all’’ and 6 means ‘‘very much’’. This

grading system has been selected because it is well known

among the patients as it is adapted from the local school

system (Table 6).

The pilot study included mobilisation therapy and ball

game therapy. The labyrinth game and the ADL training

scenarios have been tested with some healthy subjects, but

have not been evaluated with patients yet.

4 Discussion

4.1 Control scheme and stability

It has been shown that the computed torque controller

allows a precise position control in the case of an ideal

plant. The remaining error is due to the numerical impre-

cision of the calculations. In the realistic scenario, with

discrete sensor information and modelling error, the dif-

ferences between computed torque and gravity compensa-

tion with PD controller become smaller and therefore, the

gravity compensation with PD controller is a good com-

promise between calculation time and precision. The

measured position error of 0.1� for axis 1, 2 and 4 and 0.5�for axis 3 is close to the values determined by the simu-

lations and satisfactory for rehabilitation purposes.

Figure 9 shows the position error and the velocity error

when the human acts with sinusoidal disturbances at dif-

ferent frequencies. The position error does remain rather

small, except when the amplitude gains values above

25 Nm. The reason for this clear increase is that in the

simulations the actuator torques were limited to 25 Nm.

The velocity error shows more variations. These variations

became indeed observable in small, high frequency vibra-

tions that were not visible but produced an audible noise.

180 185 190 195 200 205 210 215 220 225 230-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.81

Time [s]N

orm

alis

ed le

vels

Subject A

xball

xhand

Fxref

320 325 330 335 340 345 350 355 360 365 370-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Time [s]

Nor

mal

ised

leve

ls

Subject B

xball

xhand

Fxref

Fig. 11 Movement and force

support recorded from two

different hemiplecic subjects

playing the ball game. Subject

A (upper graph) does not need

support from the robot, while

subject B (lower graph) needs

some support

Table 6 Questionnaire

Question (translated from German) Average

mark

(n = 5)

1. Was the fixation in the robot comfortable? 4.6

2. Did you like to do the mobilisation therapy? 5.25

4. Did you like to play the ball game? 5.0

3. Do you think that this sort of therapy could

help you to regain motor function?

5.25

5. What was your overall impression from the device? 5.5

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Many hours of operation with adjusted PD controller

parameters without a patient did not yield instable behav-

iour. Only high-frequency and high-amplitude external

perturbations (e.g. induced by the patient) cannot be

compensated by the controller and can cause vibrations

(Fig. 9, right).

However, assuming that the bandwidth of human arm

movement is limited to 10 Hz [30], such high-frequency

and high-amplitude perturbation cannot be induced by the

human.

This is not a strict stability proof, but both, the simu-

lation and the experimental results, support the hypothesis

that the robot stays stable when it interacts with the human

arm.

4.2 Specifications of the device

Most of the specifications for the robot (Table 1) could be

fulfilled (Table 5). The ROM of the vertical shoulder

rotation is limited by the mechanical construction of the

robot.

4.3 Pilot study

The teach-and-repeat mode allows the therapist to easily

define an appropriate movement and deliver smooth

movements. While the patient is passive during the mo-

bilisation therapy, he or she is active during the ball-game-

supported therapy. The results of the questionnaire suggest

that the patients like both, the ball game and the mobili-

sation. The patients rated their fixation in the device with

the average mark 4.6, which is significantly lower than the

other marks. As the elbow fixation did not cause any

problems, this relatively bad mark seems to be related to an

uncomfortable shoulder fixation.

In fact, the shoulder actuation module is the most critical

component (Fig. 1). It includes 3 actuated DOF and 2

passive DOF, which is advantageous in that the human

shoulder is not constrained by the robot fixation. This

allows movements of the shoulder in 3 rotary DOF with

large ROM. Although the weight of the robot has been

compensated by a passive counterbalance system, addi-

tional subject-dependent weight and active joint torques

produced by the robot will be transferred to the human

shoulder as the human arm is closing the open kinematic

chain of the robot. During standstill, the distal part of the

robot and the human arm beyond the passive axis 5 (Fig. 1)

is in a static equilibrium about axis 5. Therefore, the

resulting vertical force acting on the shoulder depends on

the robot and arm position and the weight of the mechani-

cal components and the human arm. The weight of the

distal part of the robot alone induces a vertical shoulder

force of approximately 8.5 N (robot arm in a horizontal

position, elbow extended). Furthermore, the drives of axes

1–3 produce reaction forces that are transmitted through the

shoulder joint to the environment, which further increases

the load at the shoulder.

Therefore, patients with instable shoulder joints, e.g.

resulting from shoulder subluxations, could not be treated

by the robot. Thus, our shoulder actuation approach is

acceptable for healthy subjects but problematic for patients

with shoulder problems. As many stroke patients suffer

from shoulder problems, it has been suggested to modify

the shoulder actuation module.

4.4 Prototypes

All simulations and measurements presented in this paper

have been performed with the presented 4 DOF version of

ARMin (Fig. 8). Derived from the results of this work, a

new version with 6 DOF has been set up (Fig. 12). The

robot is statically determined and equipped with a new

shoulder actuation principle. A system of linkages moves

the centre of rotation of the shoulder in the vertical plane,

when the arm is lifted. This function is required to provide

an anatomically correct shoulder movement and to avoid

the shoulder getting mechanically overstressed due to

misalignment of the technical and anatomical joint axes

when lifting the upper arm above face level. The device is

not evaluated yet.

Fig. 12 ARMin II—New version with 6DOF and statically deter-

mined shoulder actuation

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5 Conclusion and outlook

A new semi-exoskeleton arm rehabilitation robot called

ARMin has been developed and tested with numerous

stroke and SCI patients. Providing movements in four

DOF, ARMin can support spatial arm movements. A

patient-cooperative control strategy has been presented and

evaluated. It supports the patient only when necessary. In

combination with an audiovisual display ARMin can be

used to play ball games or to train ADL-related tasks.

These methods have the chance to provoke the patients to

actively contribute to the movement, increase the motiva-

tion of the patient, enhance the intensity of the training and,

thus, improve the therapeutic outcome of the therapy

compared to conventional training methods. Future tech-

nical work will focus on the evaluation of the new shoulder

actuation method and the implementation and evaluation of

further ADL training tasks. Future clinical work will focus

on clinical studies measuring the rehabilitation benefit for

the patient. It needs to be demonstrated that the therapeutic

outcome of arm therapy with the ARMin robot is higher

than the arm therapy delivered by robots with simpler

mechanics.

Acknowledgments This study was supported in part by the NCCR

for Neuroplasticity and Repair, Project 8, Switzerland. We thank

Dr. Gery Colombo from Hocoma AG, Volketswil, Switzerland for

his contribution to this work. We also thank the occupational ther-

apists and Prof. Dr. V. Dietz of the Balgrist University Hospital,

Zurich, as well as Raphael Suard, Stephane Kuhne, Christina Perndl,

Frauke Oldewurtel and Gabriela Kiefer for their contributions to this

work.

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